This LeetCode repository is organised by language first, so the Python folder is the index

Language first, difficulty second, and the tree that shows it

The folder layout is unusual for a solutions repository, and it is the first thing to understand.

leetcode-solution/
│
├── Python/
│   ├── Easy/
│   ├── Medium/
│   └── Hard/
├── C++/
├── JavaScript/
├── TypeScript/
├── SQL/
│
├── Question_List_0001_1000.md
├── Question_List_1001_2000.md
├── Question_List_2001_3000.md
└── Question_List_3001_4000.md

Language is the first level of the hierarchy, and difficulty only appears explicitly under Python. The README describes the organisation as being by difficulty and language, but the tree shows the language directories sitting at the top with the problem folders inside them.

So the navigation experience is: pick a language, then find the problem within that language, and use the four Question_List markdown files at the root to jump to a problem by number. Those lists cover problems 0001 to 1000, 1001 to 2000, 2001 to 3000 and 3001 to 4000, and they are the practical index for a collection this size.

The scope is stated in the README's own bolded line: clean, well-commented solutions for LeetCode problems #1 to 4000, written in Python, C++, JavaScript, TypeScript and SQL. The repository description frames the audience as interview prep, learning and code practice across multiple programming languages.

The MIT licence and the badges are unremarkable. What is unusual is the size of the range and the fact that the collection is presented as a browsable corpus rather than as a course.

A README in every problem folder is what makes it worth reading

The feature list in the README calls out in-depth explanations as one of the four things you will find, and this is the differentiator.

Each problem folder contains a README file with a problem description, the approach taken and a thorough walkthrough of the solution. The repository states that each solution is meticulously organized by difficulty and language, and that the READMEs explain the problem, the approach and the walkthrough.

That is a real difference from the typical solutions repository, which tends to be a folder of bare function definitions with a short comment at the top. A per-problem writeup is the difference between code you can read and code you have to trace.

The other three claimed qualities are the range of problems across topics and difficulty levels from Easy to Hard, multi-language coverage so that a reader can compare an approach across languages, and organization that makes it easy to find what you need.

The multi-language angle deserves a note, because it is the part that is hard to replicate. Seeing the same problem solved in Python and in SQL teaches something different from seeing it twice in one language, particularly for the database problems where the SQL formulation is not a translation of the Python one but a different way of expressing the constraint.

The tooling that keeps the tree tidy

Two scripts at the repository root suggest the collection is maintained mechanically rather than by hand.

There is organize.py, a Python script, and organize_and_clean.sh, a shell wrapper. Given the layout they produce, their job is to place solutions into the right folder for their language and difficulty and to clean up the result, which is the kind of chore that gets out of hand in a repository with thousands of files added by many contributors.

There is also a stats.json at the root and an assets/ directory holding the LeetCode stats graphic used in the README, which suggests the counts are generated rather than typed by hand.

The remaining root entries describe the shape of the project. There is a Readme/ directory, which is presumably where the shared documentation lives as opposed to the per-problem READMEs, an assets/ folder, a.vscode/ directory, a.github/ directory, a CODE_OF_CONDUCT.md and a LICENSE file. The package.json is minimal, with node-fetch as its only dependency and the package marked as an ES module, which is what the fetch-based scripts need and nothing more.

There are no GitHub releases, and the last push to the repository was on 2026-10-22. The project is MIT licensed.

Getting the code and working through a problem

The README walks through nine steps, and the practical parts are the first two.

You clone the repository first.

git clone https://github.com/hogan.tech/leetcode-solutions.git
cd leetcode-solution

Then you browse the folders to find the LeetCode problem you want to solve, choose a language inside the problem folder, read the README in that folder, open the code file, and run it on your local machine to test and modify it.

The last two steps are contributing and feedback. You can submit a pull request if you have a better solution, and the project asks for feedback through an Issue or on LinkedIn.

The contribution conventions are specific enough to be useful. Fork, then branch with a name like feat/add-problem-1234, then commit, then open a pull request. Follow the existing folder and naming conventions, add a short explanation either as a README or as comments, and tag the pull request with the language label.

That last convention is the one worth noting, because a single problem solved in several languages becomes several pull requests, and the language label is how a reviewer tells them apart. It is a small thing that suggests the project has dealt with the multi-language contribution problem already.

Where this repository is the wrong choice

Four situations argue against it.

The first is learning from scratch. This is an answer key, not a course. It covers problems 1 to 4000 with solutions and explanations, but there is no ordering, no progression, no prerequisite chain and no exercises with the solution hidden. If you want to learn algorithmic thinking, a platform with a curriculum and difficulty tiers per learner is a better tool, and if you want to practise, you should attempt a problem before reading the answer here rather than after.

The second is running it. There is no test suite, no build system and no CI that runs the solutions against the problem inputs. The README says you can run the code on your local machine, and you can, but nothing verifies that the solution in an older folder still passes.

The third is depending on it as a library. The package.json name is leetcode-solution and it is not published as a usable package; the npm dependency exists for the scripts. Treat it as a git clone you read from, which is what it is.

The alternative in the same direction is the LeetCode platform itself, where you get the problem statements, your own submissions with feedback, discussion threads per problem and per language, and company-tagged questions that reflect what is actually asked. The difference in approach is that this repository gives you clean, commented, offline code and a per-problem explanation you can read at your own pace, while the platform gives you immediate feedback on your own attempt. Interview preparation usually wants both: the platform to test yourself against, and a repository like this to read the reasoning behind a solution you could not derive.

Editorial conclusion

leetcode-solution fits a candidate who wants a worked explanation per problem and the option to read the same problem in several languages, since each problem folder holds a README and a file per language. It does not fit a learner who needs a curriculum, a progression or tests, because the repository is an archive of solutions rather than a course, and it does not fit a team wanting to depend on the code. Clone it and browse the Python folder by difficulty, and read the README inside a problem folder before running any solution, since the explanations are the part of this repository that cannot be generated on the fly.

Frequently asked questions

Official sources

1. hogan-tech/leetcode-solution on GitHub
2. Issues
3. License: MIT
4. Project website
5. README